Utility of adding Radiomics to clinical features in predicting the outcomes of radiotherapy for head and neck cancer using machine learning

Tarun Gangil; Krishna Sharan; B. Dinesh Rao; Krishnamoorthy Palanisamy; Biswaroop Chakrabarti; Rajagopal Kadavigere

doi:10.1371/journal.pone.0277168

Peer Review History

Original SubmissionJune 12, 2022
20 Jul 2022 Decision Letter - Panagiotis Balermpas, Editor PONE-D-22-16089Utility of adding Radiomics to clinical features in predicting outcomes of radiotherapy for Head and Neck Cancer using Machine Learning PLOS ONE Dear Dr. Kadavigere, Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process. ============================== Dear authors, the reviewers have now completed their reports and found that your manuscript in its present for is not suitbale for publication. There are some major concerns that have to be answered if you want to resubmit it: 1) There are some ethical concerns, as you state that no consent was acquired from the participants for use of the data because "the study was retrospective" 2) The methodology used is not described sufficiently 3) There is no split in training and validation set and maybe an additional cohort should be analyzed 4) The english language used is unacceptable and the manuscript should be edited by a native speaker/ scientific writer ============================== Please submit your revised manuscript by Sep 03 2022 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file. Please include the following items when submitting your revised manuscript: A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'. A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'. An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'. If you would like to make changes to your financial disclosure, please include your updated statement in your cover letter. Guidelines for resubmitting your figure files are available below the reviewer comments at the end of this letter. If applicable, we recommend that you deposit your laboratory protocols in protocols.io to enhance the reproducibility of your results. 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If your ethics statement is written in any section besides the Methods, please move it to the Methods section and delete it from any other section. Please ensure that your ethics statement is included in your manuscript, as the ethics statement entered into the online submission form will not be published alongside your manuscript. 4. Please provide additional details regarding participant consent. In the Methods section, please ensure that you have specified (1) whether consent was informed and (2) what type you obtained (for instance, written or verbal). If your study included minors, state whether you obtained consent from parents or guardians. If the need for consent was waived by the ethics committee, please include this information. 5. We note that you have indicated that data from this study are available upon request. PLOS only allows data to be available upon request if there are legal or ethical restrictions on sharing data publicly. For information on unacceptable data access restrictions, please see http://journals.plos.org/plosone/s/data-availability#loc-unacceptable-data-access-restrictions. In your revised cover letter, please address the following prompts: a) If there are ethical or legal restrictions on sharing a de-identified data set, please explain them in detail (e.g., data contain potentially identifying or sensitive patient information) and who has imposed them (e.g., an ethics committee). Please also provide contact information for a data access committee, ethics committee, or other institutional body to which data requests may be sent. b) If there are no restrictions, please upload the minimal anonymized data set necessary to replicate your study findings as either Supporting Information files or to a stable, public repository and provide us with the relevant URLs, DOIs, or accession numbers. Please see http://www.bmj.com/content/340/bmj.c181.long for guidelines on how to de-identify and prepare clinical data for publication. For a list of acceptable repositories, please see http://journals.plos.org/plosone/s/data-availability#loc-recommended-repositories. We will update your Data Availability statement on your behalf to reflect the information you provide. [Note: HTML markup is below. Please do not edit.] Reviewers' comments: Reviewer's Responses to Questions Comments to the Author 1. Is the manuscript technically sound, and do the data support the conclusions? The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented. Reviewer #1: Partly Reviewer #2: Yes ******** 2. Has the statistical analysis been performed appropriately and rigorously? Reviewer #1: N/A Reviewer #2: Yes ****** 3. Have the authors made all data underlying the findings in their manuscript fully available? The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified. Reviewer #1: No Reviewer #2: No ****** 4. Is the manuscript presented in an intelligible fashion and written in standard English? PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here. Reviewer #1: No Reviewer #2: Yes ****** 5. Review Comments to the Author Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters) Reviewer #1: Dear authors, 1. I suggest that a native speaker proofreads the manuscript. There are several grammar mistakes, and the general style could be improved. 2. Please check the row for age in the patient characteristics table. 3. Even though you point to a previous study, I find the paper is lacking some high-level explanation of the methodology (how was the train/test split performed, which feature selection algorithms were employed, what are the weights in the F1-score computation, etc.). Right now it is very hard for the reader to understand what methodology was used in this study. 4. I have concerns whether the follow-up time is too little to be clinically significant. Reviewer #2: Thanks for the interesting article evaluating the value of radiomics in CT imaging. I have a couple of questions mainly on the methodolgy, which need to be clarified. 1) How was the annotation of the PT and the LN done? Manually? By whom? was each LN delineated separatly? 2)PLease provide more details on the extraction of features: - how many features were extracted per feature type? -Which pre-processing was done? - Were cubic voxels used? - Which binning method? 3) Why did you not split the dataset into a trianing and validation dataset? Since this is not performed a cross-validation is needed. To better adress all these questions please determine the radiomics quality score: https://www.radiomics.world/rqs and put your score to the manuscript including your answers as an appendix Results: With clinico-radiomic data, the mean training and testing accuracy -> From Materials and Methods it is not clear that you have splitted in training and test set, please explain. All your results on accuracy, sensitivity,... need confidence intervals. ****** 6. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files. If you choose “no”, your identity will remain anonymous but your review may still be made public. Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy. Reviewer #1: No Reviewer #2: No ******** [NOTE: If reviewer comments were submitted as an attachment file, they will be attached to this email and accessible via the submission site. Please log into your account, locate the manuscript record, and check for the action link "View Attachments". If this link does not appear, there are no attachment files.] While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, https://pacev2.apexcovantage.com/. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. 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Revision 1
2 Sep 2022 Author Response Date: 16th August 2022 Subject: Response to the reviewers’ queries and suggestions [PONE-D-22-16089] (PONE-D-22-16089: Utility of adding Radiomics to clinical features in predicting outcomes of radiotherapy for Head and Neck Cancer using Machine Learning) The authors would like to express their profuse gratitude for the reviewers’ time and efforts, and for the excellent criticisms raised in order to facilitate the betterment of our article. The queries have been addressed in the manuscript, and the replies are stated below: Queries summarized (editor): 1) There are some ethical concerns, as you state that no consent was acquired from the participants for use of the data because "the study was retrospective" Ethical clearance for this study was obtained from our Institutional Ethics Committee (approval no. 165/2018). Since the study involved retrospective data retrieved from medical records/imaging archives, and the patients were not contacted, participation consent couldn’t be taken, and was waived off by the IEC. 2) The methodology used is not described sufficiently The methodology section has been updated to provide greater details in the original manuscript. (section 2.3.1-2.3.6)(page-3-6) 3) There is no split in training and validation set and maybe an additional cohort should be analysed. Multiple iterations of Training and testing splits at 70:30 ratio was performed within the collected dataset. The details have been added and clarified in the revised manuscript (section 2.3.3-2.3.5)(page 5-6) 4) The english language used is unacceptable and the manuscript should be edited by a native speaker/ scientific writer. The manuscript has been extensively re-written to address the grammatical and clarity errors, and has been screened through language-analysis software (professional version of Grammarly) for inaccuracies. Other observations: Your ethics statement should only appear in the Methods section of your manuscript. If your ethics statement is written in any section besides the Methods, please move it to the Methods section and delete it from any other section. Please ensure that your ethics statement is included in your manuscript, as the ethics statement entered into the online submission form will not be published alongside your manuscript. Statement moved to the methodology section as suggested.(page 4-5) Other observations: Please provide additional details regarding participant consent. In the Methods section, please ensure that you have specified (1) whether consent was informed and (2) what type you obtained (for instance, written or verbal). If your study included minors, state whether you obtained consent from parents or guardians. If the need for consent was waived by the ethics committee, please include this information. The data being retrospective, informed consent from the patients was waived off by the Institutional Ethics Committee. Its details are mentioned in the methodology section of manuscript. (page 4-5) Other observations: We note that you have indicated that data from this study are available upon request. PLOS only allows data to be available upon request if there are legal or ethical restrictions on sharing data publicly. For information on unacceptable data access restrictions, please see http://journals.plos.org/plosone/s/data-availability#loc-unacceptable-data-access-restrictions. In your revised cover letter, please address the following prompts: a) If there are ethical or legal restrictions on sharing a de-identified data set, please explain them in detail (e.g., data contain potentially identifying or sensitive patient information) and who has imposed them (e.g., an ethics committee). Please also provide contact information for a data access committee, ethics committee, or other institutional body to which data requests may be sent. b) If there are no restrictions, please upload the minimal anonymized data set necessary to replicate your study findings as either Supporting Information files or to a stable, public repository and provide us with the relevant URLs, DOIs, or accession numbers. Please see http://www.bmj.com/content/340/bmj.c181.long for guidelines on how to de-identify and prepare clinical data for publication. For a list of acceptable repositories, please see http://journals.plos.org/plosone/s/data-availability#loc-recommended-repositories. We will update your Data Availability statement on your behalf to reflect the information you provide. The collected data is the intellectual property of Manipal Academy of Higher Education, Manipal (India), and Philips, Bangalore (India), and as per the exhibit, we are not permitted to share the collected data. Moreover, we don’t have the government regulatory body (Health Ministry Screening Committee, Indian Council of Medical Research) approval for data sharing. Reviewers' comments to the authors: Reviewer #1: 1. I suggest that a native speaker proofreads the manuscript. There are several grammar mistakes, and the general style could be improved. The manuscript has been extensively re-written to address the grammatical and clarity errors, and has been screened through language-analysis software (professional version of Grammarly) for inaccuracies. 2. Please check the row for age in the patient characteristics table. The data entered was incorrect; thank you very much for bringing it to our notice. The entire table has been thoroughly reviewed and corrected.(page 6-7) 3. Even though you point to a previous study, I find the paper is lacking some high-level explanation of the methodology (how was the train/test split performed, which feature selection algorithms were employed, what are the weights in the F1-score computation, etc). Right now it is very hard for the reader to understand what methodology was used in this study. The methodology section has been updated to describe the workflow in a more coherent manner.(page 3-6) 4. I have concerns whether the follow-up time is too little to be clinically significant. We agree that the follow-up duration, while sufficient for reporting early response, is indeed an inadequate represent of the overall clinical picture. The same has been reiterated in the final paragraph of the discussion stating the limitations of our study.(page 13-14) Reviewer #2: 1) How was the annotation of the PT and the LN done? Manually? By whom? was each LN delineated separately? The annotations of PT and LN were performed using the 3D slicer tool manually by the Radiation Oncologist. The same has been mentioned in the manuscript (page 4). 2)Please provide more details on the extraction of features: - how many features were extracted per feature type? We extracted features under six major domains, including shape-based (14), gray-level dependence matrix (14), gray-level cooccurence matrix (24), first-order statistics (18), gray-level run length matrix (16), gray-level size zone (16) and neighboring gray-tone difference matrix (5). The same has been mentioned in the manuscript (page 4-5). -Which pre-processing was done? No separate pre-processing of the images was performed; radiomics features were obtained after annotating the CT images with primary and nodal volumes with the pyradiomics toolbox (an extension provided by 3D-slicer annotation tool). Prior to subjecting the data to ML training algorithms, standard scalar was performed on all the variables.(page 5-6) - Were cubic voxels used? Yes. Shape-based 3D-radiomics features were used in the analysis. - Which binning method? No binning method used, as the structured radiomics features were directly extracted from the Pyradiomics toolbox. 3) Why did you not split the dataset into a training and validation dataset? Since this is not performed a cross-validation is needed. The dataset was split into training and testing dataset in the ratio of (70:30). The details are illustrated in the updated methodology section (). Thereafter, cross-validation was performed on complete data for all the models using stratified K-fold cross validation (page 5-6) 4) To better address all these questions please determine the radiomics quality score: https://www.radiomics.world/rqs and put your score to the manuscript including your answers as an appendix. Thank you for this suggestion! On attempting the suggested questionnaire, the determined score was, unfortunately, only 45%. However, we would like to state here that at least six of the 16 questions (No.s 3,4,7,8,11 and 15) are not applicable to our study. (Appendix) 5) Results: With clinico-radiomic data, the mean training and testing accuracy->From Materials and Methods it is not clear that you have splitted in training and test set, please explain. The dataset was split into training-testing ratio of 70:30, and the means of 10 such iterations were the performance was reported by evaluating the designed model using test dataset. (page 8-12) 6) All your results on accuracy, sensitivity,... need confidence intervals. Thank you again for pointing this out! Confidence intervals values have been added to all the metrics in the original manuscript. (page 8-12) Attachments Attachment Submitted filename: Response to reviewers.docx https://doi.org/10.1371/journal.pone.0277168.r002
6 Oct 2022 Decision Letter - Panagiotis Balermpas, Editor PONE-D-22-16089R1Utility of adding Radiomics to clinical features in predicting outcomes of radiotherapy for Head and Neck Cancer using Machine LearningPLOS ONE Dear Dr. Kadavigere, Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process. ==============================Dear authors, thank you for addressing all comments. Please answer the minor issues raised from reviewer 1 and then the manuscript is suitable for acceptance. ============================== Please submit your revised manuscript by Nov 20 2022 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file. Please include the following items when submitting your revised manuscript: A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'. A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'. An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'. If you would like to make changes to your financial disclosure, please include your updated statement in your cover letter. Guidelines for resubmitting your figure files are available below the reviewer comments at the end of this letter. If applicable, we recommend that you deposit your laboratory protocols in protocols.io to enhance the reproducibility of your results. Protocols.io assigns your protocol its own identifier (DOI) so that it can be cited independently in the future. For instructions see: https://journals.plos.org/plosone/s/submission-guidelines#loc-laboratory-protocols. Additionally, PLOS ONE offers an option for publishing peer-reviewed Lab Protocol articles, which describe protocols hosted on protocols.io. Read more information on sharing protocols at https://plos.org/protocols?utm_medium=editorial-email&utm_source=authorletters&utm_campaign=protocols. We look forward to receiving your revised manuscript. Kind regards, Panagiotis Balermpas Academic Editor PLOS ONE Journal Requirements: Please review your reference list to ensure that it is complete and correct. If you have cited papers that have been retracted, please include the rationale for doing so in the manuscript text, or remove these references and replace them with relevant current references. Any changes to the reference list should be mentioned in the rebuttal letter that accompanies your revised manuscript. If you need to cite a retracted article, indicate the article’s retracted status in the References list and also include a citation and full reference for the retraction notice. Additional Editor Comments: Dear authors, thank you for addressing all comments. Please answer the minor issues raised from reviewer 1 and then the manuscript is suitable for acceptance. [Note: HTML markup is below. Please do not edit.] Reviewers' comments: Reviewer's Responses to Questions Comments to the Author 1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation. Reviewer #1: (No Response) Reviewer #2: All comments have been addressed ******** 2. Is the manuscript technically sound, and do the data support the conclusions? The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented. Reviewer #1: Partly Reviewer #2: Yes ****** 3. Has the statistical analysis been performed appropriately and rigorously? Reviewer #1: Yes Reviewer #2: Yes ****** 4. Have the authors made all data underlying the findings in their manuscript fully available? The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified. Reviewer #1: No Reviewer #2: No ****** 5. Is the manuscript presented in an intelligible fashion and written in standard English? PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here. Reviewer #1: Yes Reviewer #2: Yes ****** 6. Review Comments to the Author Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters) Reviewer #1: 1. Please check capitalization rules in English and change the capitalized letters accordingly throughout the whole manuscript (e.g., in one sentence you write "artificial intelligence" and "Machine Learning"). 2. It is not clear what you mean by "Though the minimum sample size was estimated to be 256, we included all the eligible patients treated between 2013 -2018". 3. "Synthetic samples were taken out from the original samples. The training: testing split was performed with a 70:30 ratio on the class having majority samples. The training dataset was generated by adding the train-split of majority samples to the synthetic data, and the testing dataset was generated by adding test-split of the majority samples to the original minority samples". Please rephrase this paragraph and clearly state the class imbalance on the training set and on the test set for each clinical endpoint. 4. Please specify the weights of the F1-score calculation and also provide the non-weighted F1-score. 5. The LRC model is heavily overfitted. Could you implement any measure to prevent this from happening? Such as other feature reduction techniques. Reviewer #2: Thanks. It is a pitty that the data cannot be made fully available. Please consider for th future to apply for this. I understand this is not easy in medicine but it should still be an aim. I appriciate the calculation of radiomics quality score, It has an average score, the score is important that the reader can easily understand if the manuscript is more hypothesis generating or statisticall significant. ****** 7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files. If you choose “no”, your identity will remain anonymous but your review may still be made public. Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy. Reviewer #1: No Reviewer #2: No ******** [NOTE: If reviewer comments were submitted as an attachment file, they will be attached to this email and accessible via the submission site. Please log into your account, locate the manuscript record, and check for the action link "View Attachments". If this link does not appear, there are no attachment files.] While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, https://pacev2.apexcovantage.com/. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email PLOS at figures@plos.org. Please note that Supporting Information files do not need this step. https://doi.org/10.1371/journal.pone.0277168.r003
Revision 2
21 Oct 2022 Author Response Date: Oct 06 2022 11:46AM To: "Rajagopal Kadavigere" rajarad@gmail.com From: "PLOS ONE" plosone@plos.org Subject: PLOS ONE Decision: Revision required [PONE-D-22-16089R1] PONE-D-22-16089R1 Utility of adding Radiomics to clinical features in predicting outcomes of radiotherapy for Head and Neck Cancer using Machine Learning PLOS ONE The authors would again like to thank the reviewers profusely for critically reviewing the manuscript and highlighting the points which have helped us considerably improve the quality of the manuscript. The answers to the issues raised by the reviewers have been addressed below. Please review your reference list to ensure that it is complete and correct. If you have cited papers that have been retracted, please include the rationale for doing so in the manuscript text, or remove these references and replace them with relevant current references. Any changes to the reference list should be mentioned in the rebuttal letter that accompanies your revised manuscript. If you need to cite a retracted article, indicate the article’s retracted status in the References list and also include a citation and full reference for the retraction notice. The reference list is updated and checked for its correctness. Additional Editor Comments: Dear authors, thank you for addressing all comments. Please answer the minor issues raised from reviewer 1 and then the manuscript is suitable for acceptance. [Note: HTML markup is below. Please do not edit.] Reviewers' comments: Reviewer's Responses to Questions Comments to the Author 1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation. Reviewer #1: (No Response) Reviewer #2: All comments have been addressed ________________________________________ 2. Is the manuscript technically sound, and do the data support the conclusions? The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented. Reviewer #1: Partly Reviewer #2: Yes ________________________________________ 3. Has the statistical analysis been performed appropriately and rigorously? Reviewer #1: Yes Reviewer #2: Yes ________________________________________ 4. Have the authors made all data underlying the findings in their manuscript fully available? The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified. Reviewer #1: No Reviewer #2: No ________________________________________ 5. Is the manuscript presented in an intelligible fashion and written in standard English? PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here. Reviewer #1: Yes Reviewer #2: Yes ________________________________________ 6. Review Comments to the Author Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters) Reviewer #1: 1. Please check capitalization rules in English and change the capitalized letters accordingly throughout the whole manuscript (e.g., in one sentence you write "artificial intelligence" and "Machine Learning"). The sentences were checked thoroughly for capitalization and corrected throughout the manuscript. 2. It is not clear what you mean by "Though the minimum sample size was estimated to be 256, we included all the eligible patients treated between 2013 -2018". The minimum sample size was calculated for this study as 256 using the proportions of relative hazards formula. The sample size calculation is shown in section 9.2 Appendix. This was the minimum sample size required; however, we screened all the 482 records of patients treated between 2013-18 and selected all the 311 eligible patient records, because a larger sample size is expected to provide better performing models. 3. "Synthetic samples were taken out from the original samples. The training: testing split was performed with a 70:30 ratio on the class having majority samples. The training dataset was generated by adding the train-split of majority samples to the synthetic data, and the testing dataset was generated by adding test-split of the majority samples to the original minority samples". Please rephrase this paragraph and clearly state the class imbalance on the training set and on the test set for each clinical endpoint. The paragraph has been simplified and rephrased in the manuscript. Also, the number of samples for the training and testing dataset is specified for each clinical endpoints in table 2, 3,4 and 5. 4. Please specify the weights of the F1-score calculation and also provide the non-weighted F1-score. In the manuscript, Tables 2,3,4 and 5 have been updated with Training and testing f1 score, macro f1 score and weighted f1 score for each class (label 0 and 1). The number of samples for Training and testing class label 0 and 1 serves as the weights to calculate weighted f1 score. The calculation for each performance metrics is presented in section 9.3 appendix. 5. The LRC model is heavily overfitted. Could you implement any measure to prevent this from happening? Such as other feature reduction techniques. In this research we have applied intrinsic pre-processing steps to clean the dataset and balance for the class labels. In order to prevent overfitting for locoregional recurrence models, we tried the following additional steps: 1) Computed Principal Component Analysis(PCA) for the original dataset without and with feature selection using Sequential Forward Floating Selection and visualised the results for two principal components. However, a clear class boundary which separates two variables couldn’t be drawn. The PCA plots are shown as follows: a) b) Figure: PCA plot of two components for only clinical data a) without feature selection and b) with feature selection a) b) Figure: PCA plot of two components for clinico radiomics data a) without feature selection and b) with feature selection a) b) Figure: PCA plot of two components for only radiomics data a) without feature selection and b) with feature selection 2) Originally have used the ‘accuracy’ hyperparameter for selecting optimal features. We have redone the analysis for locoregional models by changing the hyperparameter setting to ‘f1’and ‘f1_weighted’ 3) Finally, we tried to vary the learning rate ‘C’ in KSVM for Only Clinical and Clinico-Radiomics dataset and ‘max_depth’ in RF for Only Radiomics dataset. The training and testing accuracy variation with the variability of learning rate parameters is shown in the table as follows: Hyper Parameter variation Only clinical KSVM Clinico radiomics KSVM KSVM C= 0.1 Training Weighted F1:0.53 Testing Weighted F1: 0.33 Training Weighted F1: 0.44 Testing Weighted F1: 0.25 C= 0.2 Training Weighted F1:0.69 Testing Weighted F1:0.48 Training Weighted F1: 0.66 Testing Weighted F1: 0.41 C= 0.3 Training Weighted F1:0.76 Testing Weighted F1: 0.49 Training Weighted F1: 0.86 Testing Weighted F1: 0.53 C= 0.4 Training Weighted F1: 0.95 Testing Weighted F1: 0.50 Training Weighted F1: 0.99 Testing Weighted F1: 0.53 C= 0.5 Training Weighted F1: 0.96 Testing Weighted F1: 0.52 Training Weighted F1:0.99 Testing Weighted F1: 0.59 C= 0.6 Training Weighted F1:0.96 Testing Weighted F1: 0.55 Training Weighted F1: 0.99 Testing Weighted F1: 0.66 C= 0.7 Training Weighted F1: 0.97 Testing Weighted F1: 0.57 Training Weighted F1: 1 Testing Weighted F1: 0.82 C= 0.8 Training Weighted F1:0.96 Testing Weighted F1: 0.73 Training Weighted F1:1 Testing Weighted F1:0.81 C= 0.9 Training Weighted F1:0.97 Testing Weighted F1: 0.72 Training Weighted F1:1 Testing Weighted F1:0.81 C= 1.0 Training Weighted F1:0.97 Testing Weighted F1: 0.72 Training Weighted F1:1 Testing Weighted F1:0.81 C= 1000 Training Weighted F1:1 Testing Weighted F1: 0.76 Training Weighted F1:1 Testing Weighted F1:0.78 RF max_depth=5 Training Weighted F1:0.84 Testing Weighted F1: 0.65 max_depth=10 Training Weighted F1:0.86 Testing Weighted F1: 0.68 max_depth=50 Training Weighted F1:0.86 Testing Weighted F1: 0.66 max_depth=100 Training Weighted F1:0.86 Testing Weighted F1: 0.64 max_depth=1000 Training Weighted F1:0.85 Testing Weighted F1: 0.65 Thus, we concluded that class labels have high overlap due to limitation of low number of samples, the algorithm was bound to overfit. With a greater sample size and higher numbers of positive samples, it might be feasible to prevent this. Reviewer #2: Thanks. It is a pity that the data cannot be made fully available. Please consider for th future to apply for this. I understand this is not easy in medicine but it should still be an aim. I appriciate the calculation of radiomics quality score, It has an average score, the score is important that the reader can easily understand if the manuscript is more hypothesis generating or statistically significant. ________________________________________ 7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files. If you choose “no”, your identity will remain anonymous but your review may still be made public. Do you want your identity to be public for this peer review? 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Formally Accepted
17 Nov 2022 Acceptance Letter - Panagiotis Balermpas, Editor PONE-D-22-16089R2 Utility of Adding Radiomics to Clinical Features in Predicting the Outcomes of Radiotherapy for Head and Neck Cancer Using Machine Learning Dear Dr. Kadavigere: I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department. If your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information please contact onepress@plos.org. If we can help with anything else, please email us at plosone@plos.org. Thank you for submitting your work to PLOS ONE and supporting open access. Kind regards, PLOS ONE Editorial Office Staff on behalf of Dr. Panagiotis Balermpas Academic Editor PLOS ONE https://doi.org/10.1371/journal.pone.0277168.r006

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